A complex-valued slow independent component analysis based incipient fault detection and diagnosis method with applications to wastewater treatment processes

Chong Xu; Daoping Huang; Baoping Cai; Hongtian Chen; Yiqi Liu

doi:10.1016/j.isatra.2022.09.019

A complex-valued slow independent component analysis based incipient fault detection and diagnosis method with applications to wastewater treatment processes

ISA Trans. 2023 Apr:135:213-232. doi: 10.1016/j.isatra.2022.09.019. Epub 2022 Sep 17.

Authors

Chong Xu¹, Daoping Huang², Baoping Cai³, Hongtian Chen⁴, Yiqi Liu⁵

Affiliations

¹ Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, the School of Automation Science & Engineering, South China University of Technology, Guangzhou, 510640, China; School of Physics & Electronic Information, Gannan Normal University, Ganzhou 341000, China; Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, the School of Automation Science & Engineering, South China University of Technology, Guangzhou, 510640, China.
² Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, the School of Automation Science & Engineering, South China University of Technology, Guangzhou, 510640, China; Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, the School of Automation Science & Engineering, South China University of Technology, Guangzhou, 510640, China.
³ College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao, Shandong 266580, China.
⁴ Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada.
⁵ Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, the School of Automation Science & Engineering, South China University of Technology, Guangzhou, 510640, China; Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, the School of Automation Science & Engineering, South China University of Technology, Guangzhou, 510640, China. Electronic address: aulyq@scut.edu.cn.

PMID: 36175190
DOI: 10.1016/j.isatra.2022.09.019

Abstract

Multivariate statistical process monitoring are the essential approaches to achieve better prognostics and health management (PHM) of process industries. However, incipient faults and complex behaviors (such as nonlinearity and dynamics) always render the traditional multivariate statistical process monitoring approaches inadequate. Thus, a complex-valued slow independent component analysis (CSICA) is proposed, which is able to extract optimized features from a complex-valued matrix containing both of raw data and their changing rates by resorting to a complex-valued independent component analysis operation and a batch of phase shifts. These features, named slow independent components (SICs), not only guarantee the statistical independence but also capture slowly-changing patterns, thus refining both dynamic and non-Gaussian information mostly related with incipient faults. The proposed algorithm together with novel statistics, I_s², I_f² and SPE, as well as their control limits can sequentially detect incipient faults effectively. Then, together with the novel differential mapping reconstructed contribution plot (DM-RCP) and Granger causality analysis, the proposed method can accurately locate rooting causes of incipient faults. Finally, the proposed framework of process monitoring is validated through two data sets from a simulation platform and an oxidation-ditch-based wastewater treatment plant, respectively. The results demonstrate that the proposed method can achieve more accurate and efficient performances than conventional methods.

Keywords: Auto-correlation; Complex-valued slow independent component analysis; Granger causality analysis; Incipient faults; Process monitoring; Wastewater treatment processes.